Novel Gene Expression Model for Outcome Prediction in Paediatric Medulloblastoma

[1]  B. Pollo,et al.  Histological variants of medulloblastoma are the most powerful clinical prognostic indicators , 2013, Pediatric blood & cancer.

[2]  Matthew J. Betts,et al.  Dissecting the genomic complexity underlying medulloblastoma , 2012, Nature.

[3]  Elaine R. Mardis,et al.  Novel mutations target distinct subgroups of medulloblastoma , 2012, Nature.

[4]  R. Wechsler-Reya,et al.  Personalized mice: modelling the molecular heterogeneity of medulloblastoma , 2012, Neuropathology and applied neurobiology.

[5]  S. Pfister Medulloblastoma: a potpourri of distinct entities , 2012, Acta Neuropathologica.

[6]  J. Olson,et al.  The molecular classification of medulloblastoma: driving the next generation clinical trials , 2012, Current opinion in pediatrics.

[7]  O. Delattre,et al.  Prognostic classification of pediatric medulloblastoma based on chromosome 17p loss, expression of MYCC and MYCN, and Wnt pathway activation. , 2012, Neuro-oncology.

[8]  Scott L. Pomeroy,et al.  Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples , 2011, Acta Neuropathologica.

[9]  Hyung-Seok Kim,et al.  The presence of stem cell marker‐expressing cells is not prognostically significant in glioblastomas , 2011, Neuropathology : official journal of the Japanese Society of Neuropathology.

[10]  M. Kool,et al.  High levels of PROM1 (CD133) transcript are a potential predictor of poor prognosis in medulloblastoma. , 2011, Neuro-oncology.

[11]  J. Mesirov,et al.  Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  O. Chinot,et al.  Prognostic Impact of CD133 mRNA Expression in 48 Glioblastoma Patients Treated with Concomitant Radiochemotherapy: A Prospective Patient Cohort at a Single Institution , 2011, Annals of Surgical Oncology.

[13]  H. Zoghbi,et al.  In vivo Atoh1 targetome reveals how a proneural transcription factor regulates cerebellar development , 2011, Proceedings of the National Academy of Sciences.

[14]  O. Okamoto,et al.  Expression analysis of stem cell-related genes reveal OCT4 as a predictor of poor clinical outcome in medulloblastoma , 2011, Journal of Neuro-Oncology.

[15]  Yiai Tong,et al.  Subtypes of medulloblastoma have distinct developmental origins , 2010, Nature.

[16]  P. Northcott,et al.  Calculating a cure for cancer: managing medulloblastoma MATH1-ematically , 2010, Expert review of neurotherapeutics.

[17]  M. Kool,et al.  Molecular diagnostics of CNS embryonal tumors , 2010, Acta Neuropathologica.

[18]  F. Zindy,et al.  Atoh1 inhibits neuronal differentiation and collaborates with Gli1 to generate medulloblastoma-initiating cells. , 2010, Cancer research.

[19]  Serban Nacu,et al.  A hierarchy of self-renewing tumor-initiating cell types in glioblastoma. , 2010, Cancer cell.

[20]  F. Heppner,et al.  Cerebellar stem cells act as medulloblastoma-initiating cells in a mouse model and a neural stem cell signature characterizes a subset of human medulloblastomas , 2010, Oncogene.

[21]  A. Navarro,et al.  Tumour CD133 mRNA expression and clinical outcome in surgically resected colorectal cancer patients. , 2010, European journal of cancer.

[22]  Huda Y Zoghbi,et al.  Deletion of Atoh1 Disrupts Sonic Hedgehog Signaling in the Developing Cerebellum and Prevents Medulloblastoma , 2009, Science.

[23]  V. Sottile,et al.  Dynamic distribution and stem cell characteristics of Sox1-expressing cells in the cerebellar cortex , 2009, Cell Research.

[24]  P. Febbo,et al.  Identification of CD15 as a marker for tumor-propagating cells in a mouse model of medulloblastoma. , 2009, Cancer cell.

[25]  C. Eberhart,et al.  Expression of p75NTR in fetal brain and medulloblastomas: evidence of a precursor cell marker and its persistence in neoplasia , 2009, Journal of Neuro-Oncology.

[26]  C. Eberhart,et al.  Just say no to ATOH: how HIC1 methylation might predispose medulloblastoma to lineage addiction. , 2008, Cancer research.

[27]  M. D. Den Boer,et al.  Differential expression and prognostic significance of SOX genes in pediatric medulloblastoma and ependymoma identified by microarray analysis. , 2008, Neuro-oncology.

[28]  Dirk Troost,et al.  Integrated Genomics Identifies Five Medulloblastoma Subtypes with Distinct Genetic Profiles, Pathway Signatures and Clinicopathological Features , 2008, PloS one.

[29]  C. Eberhart,et al.  Medulloblastoma stem cells. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  F. Zindy,et al.  Post-transcriptional down-regulation of Atoh1/Math1 by bone morphogenic proteins suppresses medulloblastoma development. , 2008, Genes & development.

[31]  P. Lichter,et al.  Stem Cell Marker CD133 Affects Clinical Outcome in Glioma Patients , 2008, Clinical Cancer Research.

[32]  G. Finocchiaro,et al.  Expression of the neurogenic basic helix-loop-helix transcription factor NEUROG1 identifies a subgroup of medulloblastomas not expressing ATOH1. , 2007, Neuro-oncology.

[33]  R. Mrak,et al.  Neurotrophin receptors and heparanase: a functional axis in human medulloblastoma invasion. , 2007, Journal of experimental & clinical cancer research : CR.

[34]  G. Maki,et al.  Daoy medulloblastoma cells that express CD133 are radioresistant relative to CD133- cells, and the CD133+ sector is enlarged by hypoxia. , 2007, International journal of radiation oncology, biology, physics.

[35]  D. Louis WHO classification of tumours of the central nervous system , 2007 .

[36]  Mark W. Dewhirst,et al.  Glioma stem cells promote radioresistance by preferential activation of the DNA damage response , 2006, Nature.

[37]  M. Kool,et al.  OTX1 and OTX2 Expression Correlates With the Clinicopathologic Classification of Medulloblastomas , 2006, Journal of neuropathology and experimental neurology.

[38]  Claus Lindbjerg Andersen,et al.  Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets , 2004, Cancer Research.

[39]  F. Speleman,et al.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes , 2002, Genome Biology.

[40]  M. Pfaffl,et al.  A new mathematical model for relative quantification in real-time RT-PCR. , 2001, Nucleic acids research.

[41]  Jane E. Johnson,et al.  Overexpression of MATH1 Disrupts the Coordination of Neural Differentiation in Cerebellum Development , 2001, Molecular and Cellular Neuroscience.